Distinguishing atypical parotid carcinomas and pleomorphic adenomas based on multiphasic computed tomography radiomics nomogram: a multicenter study.

Objective: This study aimed to develop, validate, and test a comprehensive radiomics prediction model using clinical data and contrast-enhanced multiphasic computed tomography (CT) scans for differentiating between atypical parotid carcinomas (PCAs) and pleomorphic adenomas (PAs) within a multicenter cohort. Materials and methods: The study involved 218 patients diagnosed with either PAs (n=162) or atypical PCAs (n=56) (no invasion of adjacent tissues or lymph node metastases) across three anonymized hospitals, divided into a training set (n=175) and a validation set (n=43). Clinical features and radiological findings were used to develop a clinical model. Radiomics features were extracted from multi-phase contrast-enhanced CT, with feature selection achieved through statistical methods and the least absolute shrinkage and selection operator (LASSO). Radiomics signature were developed using a Light Gradient Boosting Decision Tree (LightGBM) model. A radiomics nomogram integrating significant clinical risk factors with the radiomics signature was created, with external validation conducted on an independent dataset of 32 patients from two additional hospitals. Results: In the training set, the multiphase models (model Conclusion: The CT-based radiomics nomogram, supplemented by machine learning, effectively differentiates atypical PCAs from PAs, presenting a non-invasive diagnostic tool that could guide treatment decisions and reduce the need for invasive procedures.